Fairness in machine learning has been a research subject with rapid growth recently. Many different definitions of fairness have been designed to fit different settings, e.g., equality of opportunity and equalized odds [1]}, direct and indirect discrimination [2]}, [3]}, [4]}, counterfactual fairness [5]}, [6]}, [7]},
and path-specific counterfactual fairness [8]}. Although there are many works focusing on fairness in personalized recommendation [9]}, [10]}, [11]}, how to achieve individual fairness in bandit recommendation still remains a challenging task.
